RANK-BASED INFERENCE FOR MULTIVARIATE NONLINEAR AND LONG-MEMORY TIME SERIES MODELS

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概要

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• The portfolio of the Japanese Government Pension Investment Fund (GPIF) consists of a linear combination of five benchmarks of financial assets. Some of these exhibit long-memory and nonlinear behavior. Their analysis therefore requires multivariate nonlinear and long-memory time series models. Moreover, the assumption that the innovation densities underlying those models are known seems quite unrealistic. If those densities remain unspecified, the model becomes a semiparametric one, and rank-based inference methods naturally come into the picture. Rank-based inference methods under very general conditions are known to achieve the semiparametric efficiency bounds. % through the maximum invariant property of ranks. Defining ranks in the context of multivariate time series models, however, is not obvious. We propose two distinct definitions. The first one relies on the assumption that the innovation density is some unspecified elliptical density. The second one relies on the assumption that the innovation process is described by some unspecified independent component analysis model. Applications to portfolio management problems are discussed.

• 2010-06-01

著者

• Taniguchi Masanobu

  Department Of Applied Mathematics School Of Fundamental Science And Engineering Waseda University

• Hirukawa Junichi

  Department Of Mathematics Faculty Of Science Niigata University

• Hirukawa Junichi

  Department Of Mathematical Sciences School Of Science & Engineering Waseda University

• TANIAI Hiroyuki

  School of International Liberal Studies, Waseda University

• HALLIN Marc

  Institut de Recherche en Statistique, ECARES, Universite libre de Bruxelles

• Hallin Marc

  Institut De Recherche En Statistique Ecares Universite Libre De Bruxelles

• Taniai Hiroyuki

  School Of International Liberal Studies Waseda University

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• DYNAMIC PORTFOLIO OPTIMIZATION USING GENERALIZED DYNAMIC CONDITIONAL HETEROSKEDASTIC FACTOR MODELS
• RANK-BASED INFERENCE FOR MULTIVARIATE NONLINEAR AND LONG-MEMORY TIME SERIES MODELS
• AN ESTIMATION METHOD IN TIME SERIES ERRORS-IN-VARIABLES MODELS
• DISCRIMINANT ANALYSIS FOR MULTIVARIATE NON-GAUSSIAN LOCALLY STATIONARY PROCESSES
• GENERALIZED INFORMATION CRITERIA IN MODEL SELECTION FOR LOCALLY STATIONARY PROCESSES
• GENERALIZED INFORMATION CRITERIA IN MODEL SELECTION FOR LOCALLY STATIONARY PROCESSES

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